Overview

Dataset statistics

Number of variables7
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.0 KiB
Average record size in memory64.0 B

Variable types

Numeric6
Categorical1

Reproduction

Analysis started2024-05-24 14:56:48.840648
Analysis finished2024-05-24 14:56:57.472001
Duration8.63 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Glucose
Real number (ℝ)

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.39761
Minimum24
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2024-05-24T20:26:57.665522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72.119492
Q380
95-th percentile90
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.096396
Coefficient of variation (CV)0.16708281
Kurtosis1.0980642
Mean72.39761
Median Absolute Deviation (MAD)7.8805085
Skewness0.13918694
Sum55601.364
Variance146.32279
MonotonicityNot monotonic
2024-05-24T20:26:58.038808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
70 57
 
7.4%
74 52
 
6.8%
68 45
 
5.9%
78 45
 
5.9%
72 44
 
5.7%
64 43
 
5.6%
80 40
 
5.2%
76 39
 
5.1%
60 37
 
4.8%
72.23898305 35
 
4.6%
Other values (37) 331
43.1%
ValueCountFrequency (%)
24 1
 
0.1%
30 2
 
0.3%
38 1
 
0.1%
40 1
 
0.1%
44 4
 
0.5%
46 2
 
0.3%
48 5
 
0.7%
50 13
1.7%
52 11
1.4%
54 11
1.4%
ValueCountFrequency (%)
122 1
 
0.1%
114 1
 
0.1%
110 3
0.4%
108 2
0.3%
106 3
0.4%
104 2
0.3%
102 1
 
0.1%
100 3
0.4%
98 3
0.4%
96 4
0.5%

BloodPressure
Real number (ℝ)

Distinct186
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.95509
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2024-05-24T20:26:58.363711image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile50
Q1121.5
median154.33025
Q3154.33025
95-th percentile293
Maximum846
Range832
Interquartile range (IQR)32.830247

Descriptive statistics

Standard deviation85.02329
Coefficient of variation (CV)0.54869632
Kurtosis15.268338
Mean154.95509
Median Absolute Deviation (MAD)3.6697531
Skewness3.039833
Sum119005.51
Variance7228.9599
MonotonicityNot monotonic
2024-05-24T20:26:58.699693image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154.3302469 374
48.7%
105 11
 
1.4%
140 9
 
1.2%
130 9
 
1.2%
120 8
 
1.0%
100 7
 
0.9%
94 7
 
0.9%
180 7
 
0.9%
135 6
 
0.8%
115 6
 
0.8%
Other values (176) 324
42.2%
ValueCountFrequency (%)
14 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
18 2
0.3%
22 1
 
0.1%
23 2
0.3%
25 1
 
0.1%
29 1
 
0.1%
32 1
 
0.1%
36 3
0.4%
ValueCountFrequency (%)
846 1
0.1%
744 1
0.1%
680 1
0.1%
600 1
0.1%
579 1
0.1%
545 1
0.1%
543 1
0.1%
540 1
0.1%
510 1
0.1%
495 2
0.3%

Insulin
Real number (ℝ)

Distinct248
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.455956
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2024-05-24T20:26:59.025913image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.235
Q127.5
median32.352224
Q336.6
95-th percentile44.395
Maximum67.1
Range48.9
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.8751627
Coefficient of variation (CV)0.21183054
Kurtosis0.9199919
Mean32.455956
Median Absolute Deviation (MAD)4.5522241
Skewness0.59890908
Sum24926.174
Variance47.267862
MonotonicityNot monotonic
2024-05-24T20:26:59.408041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 13
 
1.7%
31.2 12
 
1.6%
31.6 12
 
1.6%
32.35222405 11
 
1.4%
33.3 10
 
1.3%
32.4 10
 
1.3%
32.8 9
 
1.2%
30.1 9
 
1.2%
32.9 9
 
1.2%
30.8 9
 
1.2%
Other values (238) 664
86.5%
ValueCountFrequency (%)
18.2 3
0.4%
18.4 1
 
0.1%
19.1 1
 
0.1%
19.3 1
 
0.1%
19.4 1
 
0.1%
19.5 2
0.3%
19.6 3
0.4%
19.9 1
 
0.1%
20 1
 
0.1%
20.1 1
 
0.1%
ValueCountFrequency (%)
67.1 1
0.1%
59.4 1
0.1%
57.3 1
0.1%
55 1
0.1%
53.2 1
0.1%
52.9 1
0.1%
52.3 2
0.3%
50 1
0.1%
49.7 1
0.1%
49.6 1
0.1%

BMI
Real number (ℝ)

Distinct136
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.89453
Minimum0
Maximum199
Zeros5
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2024-05-24T20:26:59.791840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q199
median117
Q3140.25
95-th percentile181
Maximum199
Range199
Interquartile range (IQR)41.25

Descriptive statistics

Standard deviation31.972618
Coefficient of variation (CV)0.26446703
Kurtosis0.64077982
Mean120.89453
Median Absolute Deviation (MAD)20
Skewness0.1737535
Sum92847
Variance1022.2483
MonotonicityNot monotonic
2024-05-24T20:27:00.173284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 17
 
2.2%
100 17
 
2.2%
106 14
 
1.8%
111 14
 
1.8%
129 14
 
1.8%
125 14
 
1.8%
105 13
 
1.7%
95 13
 
1.7%
108 13
 
1.7%
102 13
 
1.7%
Other values (126) 626
81.5%
ValueCountFrequency (%)
0 5
0.7%
44 1
 
0.1%
56 1
 
0.1%
57 2
 
0.3%
61 1
 
0.1%
62 1
 
0.1%
65 1
 
0.1%
67 1
 
0.1%
68 3
0.4%
71 4
0.5%
ValueCountFrequency (%)
199 1
 
0.1%
198 1
 
0.1%
197 4
0.5%
196 3
0.4%
195 2
0.3%
194 3
0.4%
193 2
0.3%
191 1
 
0.1%
190 1
 
0.1%
189 4
0.5%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2024-05-24T20:27:00.614830image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.3313286
Coefficient of variation (CV)0.70215138
Kurtosis5.5949535
Mean0.4718763
Median Absolute Deviation (MAD)0.1675
Skewness1.9199111
Sum362.401
Variance0.10977864
MonotonicityNot monotonic
2024-05-24T20:27:01.030789image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.254 6
 
0.8%
0.258 6
 
0.8%
0.261 5
 
0.7%
0.207 5
 
0.7%
0.259 5
 
0.7%
0.238 5
 
0.7%
0.268 5
 
0.7%
0.304 4
 
0.5%
0.299 4
 
0.5%
0.692 4
 
0.5%
Other values (507) 719
93.6%
ValueCountFrequency (%)
0.078 1
0.1%
0.084 1
0.1%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.1%
0.092 1
0.1%
0.096 1
0.1%
0.1 1
0.1%
0.101 1
0.1%
0.102 1
0.1%
ValueCountFrequency (%)
2.42 1
0.1%
2.329 1
0.1%
2.288 1
0.1%
2.137 1
0.1%
1.893 1
0.1%
1.781 1
0.1%
1.731 1
0.1%
1.699 1
0.1%
1.698 1
0.1%
1.6 1
0.1%

Age
Real number (ℝ)

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.240885
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2024-05-24T20:27:01.345038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.760232
Coefficient of variation (CV)0.35378816
Kurtosis0.64315889
Mean33.240885
Median Absolute Deviation (MAD)7
Skewness1.1295967
Sum25529
Variance138.30305
MonotonicityNot monotonic
2024-05-24T20:27:01.690034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 72
 
9.4%
21 63
 
8.2%
25 48
 
6.2%
24 46
 
6.0%
23 38
 
4.9%
28 35
 
4.6%
26 33
 
4.3%
27 32
 
4.2%
29 29
 
3.8%
31 24
 
3.1%
Other values (42) 348
45.3%
ValueCountFrequency (%)
21 63
8.2%
22 72
9.4%
23 38
4.9%
24 46
6.0%
25 48
6.2%
26 33
4.3%
27 32
4.2%
28 35
4.6%
29 29
3.8%
30 21
 
2.7%
ValueCountFrequency (%)
81 1
 
0.1%
72 1
 
0.1%
70 1
 
0.1%
69 2
0.3%
68 1
 
0.1%
67 3
0.4%
66 4
0.5%
65 3
0.4%
64 1
 
0.1%
63 4
0.5%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
501 
1
267 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 501
65.2%
1 267
34.8%

Length

2024-05-24T20:27:01.958674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-24T20:27:02.162216image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 501
65.2%
1 267
34.8%

Most occurring characters

ValueCountFrequency (%)
0 501
65.2%
1 267
34.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 768
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 501
65.2%
1 267
34.8%

Most occurring scripts

ValueCountFrequency (%)
Common 768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 501
65.2%
1 267
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 501
65.2%
1 267
34.8%

Interactions

2024-05-24T20:26:55.476949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:49.043939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:50.362951image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:51.525067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:52.778622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:54.050233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:55.722532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:49.302035image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:50.565699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:51.715963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:52.996426image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:54.271046image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:55.947535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:49.513424image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:50.739286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:51.934532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:53.197034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:54.524632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:56.166242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:49.701682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:50.932756image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:52.134898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:53.385224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:54.745385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:56.477526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:49.902878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:51.133449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:52.331242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:53.646587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:54.986952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:56.730657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:50.162902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:51.336151image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:52.575604image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:53.854783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-24T20:26:55.216538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-05-24T20:26:57.053530image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-24T20:26:57.350316image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GlucoseBloodPressureInsulinBMIDiabetesPedigreeFunctionAgeOutcome
072.238983154.33024732.35222484.00.30421.01
058.000000190.00000034.00000098.00.43043.01
182.000000154.33024728.200000112.01.28250.00
175.000000154.33024735.700000112.00.14821.00
246.00000083.00000028.700000139.00.65422.01
264.000000154.33024730.800000108.00.15821.01
350.000000154.33024721.900000161.00.25465.00
380.000000154.33024724.600000107.00.85634.00
480.000000370.00000046.200000134.00.23846.01
490.000000154.33024729.900000136.00.21050.01
GlucoseBloodPressureInsulinBMIDiabetesPedigreeFunctionAgeOutcome
60452.00000036.00000027.874.00.26922.01
60564.000000154.33024734.2111.00.26024.00
60674.000000144.00000036.1138.00.55750.01
60788.000000235.00000039.3126.00.70427.00
60876.000000200.00000035.9122.00.48326.00
60964.000000140.00000028.6139.00.41126.00
610122.000000154.33024722.496.00.20727.00
61186.000000154.33024745.6101.01.13638.01
61272.238983154.33024742.4141.00.20529.01
61396.000000154.33024722.5125.00.26221.00